id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.15760 | Investigating Application of Deep Neural Networks in Intrusion Detection
System Design | [
"cs.CR",
"cs.LG"
] | Despite decades of development, existing IDSs still face challenges in improving detection accuracy, evasion, and detection of unknown attacks. To solve these problems, many researchers have focused on designing and developing IDSs that use Deep Neural Networks (DNN) which provides advanced methods of threat investigat... | {
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2501.15763 | NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose
Estimation | [
"cs.CV"
] | The widespread application of 3D human pose estimation (HPE) is limited by resource-constrained edge devices, requiring more efficient models. A key approach to enhancing efficiency involves designing networks based on the structural characteristics of input data. However, effectively utilizing the structural priors in... | {
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2501.15767 | Formal Verification of Markov Processes with Learned Parameters | [
"cs.LG",
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"math.OC"
] | We introduce the problem of formally verifying properties of Markov processes where the parameters are the output of machine learning models. Our formulation is general and solves a wide range of problems, including verifying properties of probabilistic programs that use machine learning, and subgroup analysis in healt... | {
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2501.15768 | Error-State LQR Formulation for Quadrotor UAV Trajectory Tracking | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This article presents an error-state Linear Quadratic Regulator (LQR) formulation for robust trajectory tracking in quadrotor Unmanned Aerial Vehicles (UAVs). The proposed approach leverages error-state dynamics and employs exponential coordinates to represent orientation errors, enabling a linearized system representa... | {
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2501.15773 | Is It Navajo? Accurate Language Detection in Endangered Athabaskan
Languages | [
"cs.CL"
] | Endangered languages, such as Navajo - the most widely spoken Native American language - are significantly underrepresented in contemporary language technologies, exacerbating the challenges of their preservation and revitalization. This study evaluates Google's Language Identification (LangID) tool, which does not cur... | {
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2501.15774 | Efficient Attention-Sharing Information Distillation Transformer for
Lightweight Single Image Super-Resolution | [
"cs.CV"
] | Transformer-based Super-Resolution (SR) methods have demonstrated superior performance compared to convolutional neural network (CNN)-based SR approaches due to their capability to capture long-range dependencies. However, their high computational complexity necessitates the development of lightweight approaches for pr... | {
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2501.15775 | Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image
Models? | [
"cs.CV",
"cs.SE"
] | Text-to-Image (T2I) models have recently gained significant attention due to their ability to generate high-quality images and are consequently used in a wide range of applications. However, there are concerns about the gender bias of these models. Previous studies have shown that T2I models can perpetuate or even ampl... | {
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2501.15777 | Automatic Feedback Generation for Short Answer Questions using Answer
Diagnostic Graphs | [
"cs.CL"
] | Short-reading comprehension questions help students understand text structure but lack effective feedback. Students struggle to identify and correct errors, while manual feedback creation is labor-intensive. This highlights the need for automated feedback linking responses to a scoring rubric for deeper comprehension. ... | {
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2501.15781 | Large Language Models to Diffusion Finetuning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We propose a new finetuning method to provide pre-trained large language models (LMs) the ability to scale test-time compute through the diffusion framework. By increasing the number of diffusion steps, we show our finetuned models achieve monotonically increasing accuracy, directly translating to improved performance ... | {
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2501.15785 | Memorization and Regularization in Generative Diffusion Models | [
"cs.LG",
"math.DS",
"math.OC"
] | Diffusion models have emerged as a powerful framework for generative modeling. At the heart of the methodology is score matching: learning gradients of families of log-densities for noisy versions of the data distribution at different scales. When the loss function adopted in score matching is evaluated using empirical... | {
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2501.15790 | Enhancing Synthetic Oversampling for Imbalanced Datasets Using
Proxima-Orion Neighbors and q-Gaussian Weighting Technique | [
"cs.LG",
"stat.ML"
] | In this article, we propose a novel oversampling algorithm to increase the number of instances of minority class in an imbalanced dataset. We select two instances, Proxima and Orion, from the set of all minority class instances, based on a combination of relative distance weights and density estimation of majority clas... | {
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2501.15791 | Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced
Error Detection in Knowledge Graphs | [
"cs.AI",
"cs.MA"
] | Knowledge graphs are widely used in industrial applications, making error detection crucial for ensuring the reliability of downstream applications. Existing error detection methods often fail to effectively utilize fine-grained subgraph information and rely solely on fixed graph structures, while also lacking transpar... | {
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2501.15795 | Can Multimodal Large Language Models be Guided to Improve Industrial
Anomaly Detection? | [
"cs.CV"
] | In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and opera... | {
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2501.15797 | LemmaHead: RAG Assisted Proof Generation Using Large Language Models | [
"cs.LG",
"cs.CL",
"cs.IR"
] | Developing the logic necessary to solve mathematical problems or write mathematical proofs is one of the more difficult objectives for large language models (LLMS). Currently, the most popular methods in literature consists of fine-tuning the model on written mathematical content such as academic publications and textb... | {
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2501.15798 | MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus
Vision-Language Pretraining | [
"cs.CV"
] | Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievements, they significantly rely on large-scale private image-text data but pay less attention to the pretraining manner, which limits their fu... | {
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2501.15799 | Can Molecular Evolution Mechanism Enhance Molecular Representation? | [
"q-bio.BM",
"cs.LG",
"cs.NE"
] | Molecular evolution is the process of simulating the natural evolution of molecules in chemical space to explore potential molecular structures and properties. The relationships between similar molecules are often described through transformations such as adding, deleting, and modifying atoms and chemical bonds, reflec... | {
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2501.15802 | Adaptive AI-based Decentralized Resource Management in the Cloud-Edge
Continuum | [
"cs.DC",
"cs.AI"
] | The increasing complexity of application requirements and the dynamic nature of the Cloud-Edge Continuum present significant challenges for efficient resource management. These challenges stem from the ever-changing infrastructure, which is characterized by additions, removals, and reconfigurations of nodes and links, ... | {
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2501.15806 | Autonomous Horizon-based Asteroid Navigation With
Observability-constrained Maneuvers | [
"cs.RO",
"math.OC"
] | Asteroid exploration is a pertinent challenge due to the varying complexity of their dynamical environments, shape and communication delays due to distance. Thus, autonomous navigation methods are continually being developed and improved in current research to enable their safe exploration. These methods often involve ... | {
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2501.15808 | ClearSight: Human Vision-Inspired Solutions for Event-Based Motion
Deblurring | [
"cs.CV"
] | Motion deblurring addresses the challenge of image blur caused by camera or scene movement. Event cameras provide motion information that is encoded in the asynchronous event streams. To efficiently leverage the temporal information of event streams, we employ Spiking Neural Networks (SNNs) for motion feature extractio... | {
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2501.15816 | AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in
Recommendation System | [
"cs.IR",
"cs.AI"
] | Feature modeling, which involves feature representation learning and leveraging, plays an essential role in industrial recommendation systems. However, the data distribution in real-world applications usually follows a highly skewed long-tail pattern due to the popularity bias, which easily leads to over-reliance on ID... | {
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2501.15817 | Long-Term Interest Clock: Fine-Grained Time Perception in Streaming
Recommendation System | [
"cs.IR",
"cs.AI"
] | User interests manifest a dynamic pattern within the course of a day, e.g., a user usually favors soft music at 8 a.m. but may turn to ambient music at 10 p.m. To model dynamic interests in a day, hour embedding is widely used in traditional daily-trained industrial recommendation systems. However, its discreteness can... | {
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2501.15820 | FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control
Works in Real Cities | [
"eess.SY",
"cs.AI",
"cs.SY"
] | Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC. However, existing RL algorithms face several real-world challenges that hinder their practical deployment in TSC: (1) Sensor accuracy deteri... | {
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2501.15826 | MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral
Mental Health Question Answer | [
"cs.CL"
] | The Mental Health Question Answer (MHQA) task requires the seeker and supporter to complete the support process in one-turn dialogue. Given the richness of help-seeker posts, supporters must thoroughly understand the content and provide logical, comprehensive, and well-structured responses. Previous works in MHQA mostl... | {
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2501.15828 | Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing
Recovery Rate Predictions | [
"q-fin.CP",
"cs.LG",
"quant-ph"
] | Recovery rate prediction plays a pivotal role in bond investment strategies, enhancing risk assessment, optimizing portfolio allocation, improving pricing accuracy, and supporting effective credit risk management. However, forecasting faces challenges like high-dimensional features, small sample sizes, and overfitting.... | {
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2501.15830 | SpatialVLA: Exploring Spatial Representations for Visual-Language-Action
Model | [
"cs.RO",
"cs.AI"
] | In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundation model. Specifically, we introduce Ego3D Position Encoding to inject 3D information into the input observations of the visual-language-act... | {
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2501.15831 | Pfungst and Clever Hans: Identifying the unintended cues in a widely
used Alzheimer's disease MRI dataset using explainable deep learning | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Backgrounds. Deep neural networks have demonstrated high accuracy in classifying Alzheimer's disease (AD). This study aims to enlighten the underlying black-box nature and reveal individual contributions of T1-weighted (T1w) gray-white matter texture, volumetric information and preprocessing on classification perform... | {
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2501.15833 | Mode Switching-Induced Instability of Multi-source Feed DC Microgrid | [
"eess.SY",
"cs.SY"
] | In DC microgrids (DCMGs), DC-bus signaling based control strategy is extensively used for power management, where mode switching plays a crucial role in achieving multi-source coordination. However, few studies have noticed the impact of mode switching and switching strategies on system voltage stability. To fill this ... | {
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2501.15836 | Intelligent Code Embedding Framework for High-Precision Ransomware
Detection via Multimodal Execution Path Analysis | [
"cs.CR",
"cs.AI"
] | Modern threat landscapes continue to evolve with increasing sophistication, challenging traditional detection methodologies and necessitating innovative solutions capable of addressing complex adversarial tactics. A novel framework was developed to identify ransomware activity through multimodal execution path analysis... | {
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2501.15838 | CrySPAI: A new Crystal Structure Prediction Software Based on Artificial
Intelligence | [
"cond-mat.mtrl-sci",
"cs.AI"
] | Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this pap... | {
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2501.15839 | Controllable Hand Grasp Generation for HOI and Efficient Evaluation
Methods | [
"cs.CV"
] | Controllable affordance Hand-Object Interaction (HOI) generation has become an increasingly important area of research in computer vision. In HOI generation, the hand grasp generation is a crucial step for effectively controlling the geometry of the hand. Current hand grasp generation methods rely on 3D information for... | {
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2501.15842 | Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory
Prediction Robustness | [
"cs.LG",
"cs.AI",
"stat.ML"
] | We study the Out-of-Distribution (OoD) generalization ability of three SotA trajectory prediction models with comparable In-Distribution (ID) performance but different model designs. We investigate the influence of inductive bias, size of training data and data augmentation strategy by training the models on Argoverse ... | {
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2501.15847 | Can Location Embeddings Enhance Super-Resolution of Satellite Imagery? | [
"cs.CV"
] | Publicly available satellite imagery, such as Sentinel- 2, often lacks the spatial resolution required for accurate analysis of remote sensing tasks including urban planning and disaster response. Current super-resolution techniques are typically trained on limited datasets, leading to poor generalization across divers... | {
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2501.15849 | Gaussian Process-Based Prediction and Control of Hammerstein-Wiener
Systems | [
"eess.SY",
"cs.LG",
"cs.SY"
] | This work investigates data-driven prediction and control of Hammerstein-Wiener systems using physics-informed Gaussian process models. Data-driven prediction algorithms have been developed for structured nonlinear systems based on Willems' fundamental lemma. However, existing frameworks cannot treat output nonlinearit... | {
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2501.15850 | LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for
Autonomous Driving with Large Language Models | [
"cs.LG",
"cs.CV",
"cs.RO"
] | Ensuring and improving the safety of autonomous driving systems (ADS) is crucial for the deployment of highly automated vehicles, especially in safety-critical events. To address the rarity issue, adversarial scenario generation methods are developed, in which behaviors of traffic participants are manipulated to induce... | {
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2501.15851 | Coding for Strand Breaks in Composite DNA | [
"cs.IT",
"math.IT"
] | Even tough DNA can be considered as a very stable long term storage medium, errors must be expected during storage. From experiments it is evident that the most common error type due to storage are strand breaks. We address the problem of correcting strand breaks in DNA sequences resulting from composite DNA synthesis.... | {
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2501.15852 | CausalSR: Structural Causal Model-Driven Super-Resolution with
Counterfactual Inference | [
"cs.CV"
] | Physical and optical factors interacting with sensor characteristics create complex image degradation patterns. Despite advances in deep learning-based super-resolution, existing methods overlook the causal nature of degradation by adopting simplistic black-box mappings. This paper formulates super-resolution using str... | {
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2501.15857 | Are Transformers Able to Reason by Connecting Separated Knowledge in
Training Data? | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Humans exhibit remarkable compositional reasoning by integrating knowledge from various sources. For example, if someone learns ( B = f(A) ) from one source and ( C = g(B) ) from another, they can deduce ( C=g(B)=g(f(A)) ) even without encountering ( ABC ) together, showcasing the generalization ability of human intell... | {
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2501.15858 | Potential Applications of Artificial Intelligence for Cross-language
Intelligibility Assessment of Dysarthric Speech | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Purpose: This commentary introduces how artificial intelligence (AI) can be leveraged to advance cross-language intelligibility assessment of dysarthric speech. Method: We propose a conceptual framework consisting of a universal model that captures language-universal speech impairments and a language-specific intelligi... | {
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2501.15860 | The Components of Collaborative Joint Perception and Prediction -- A
Conceptual Framework | [
"cs.CV"
] | Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To reduce cumulative errors in perception modules and mitigate the visual occlusion, this paper introduces a new task, Collaborative Joint Perc... | {
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2501.15865 | Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis
of the Benefits and What to Share | [
"quant-ph",
"cs.AI",
"cs.ET"
] | Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called Reverse Annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewh... | {
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2501.15870 | D-PLS: Decoupled Semantic Segmentation for
4D-Panoptic-LiDAR-Segmentation | [
"cs.CV",
"cs.AI"
] | This paper introduces a novel approach to 4D Panoptic LiDAR Segmentation that decouples semantic and instance segmentation, leveraging single-scan semantic predictions as prior information for instance segmentation. Our method D-PLS first performs single-scan semantic segmentation and aggregates the results over time, ... | {
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2501.15871 | Transient Finite Element Simulation of Accelerator Magnets Using Thermal
Thin Shell Approximation | [
"physics.acc-ph",
"cond-mat.supr-con",
"cs.CE",
"physics.comp-ph"
] | Thermal transient responses of superconducting magnets can be simulated using the finite element (FE) method. Some accelerator magnets use cables whose electric insulation is significantly thinner than the bare electric conductor. The FE discretisation of such geometries with high-quality meshes leads to many degrees o... | {
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2501.15875 | LCTG Bench: LLM Controlled Text Generation Benchmark | [
"cs.CL"
] | The rise of large language models (LLMs) has led to more diverse and higher-quality machine-generated text. However, their high expressive power makes it difficult to control outputs based on specific business instructions. In response, benchmarks focusing on the controllability of LLMs have been developed, but several... | {
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2501.15876 | Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles:
A Hierarchical Framework for Enhanced NLP Tasks | [
"cs.CL",
"cs.AI"
] | Sentence embedding tasks are important in natural language processing (NLP), but improving their performance while keeping them reliable is still hard. This paper presents a framework that combines pseudo-label generation and model ensemble techniques to improve sentence embeddings. We use external data from SimpleWiki... | {
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2501.15877 | Boli: A dataset for understanding stuttering experience and analyzing
stuttered speech | [
"cs.HC",
"cs.AI"
] | There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in Indi... | {
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2501.15878 | Slot-Guided Adaptation of Pre-trained Diffusion Models for
Object-Centric Learning and Compositional Generation | [
"cs.CV",
"cs.LG"
] | We present SlotAdapt, an object-centric learning method that combines slot attention with pretrained diffusion models by introducing adapters for slot-based conditioning. Our method preserves the generative power of pretrained diffusion models, while avoiding their text-centric conditioning bias. We also incorporate an... | {
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2501.15880 | Movable Antennas Meet Intelligent Reflecting Surface: Friends or Foes? | [
"cs.IT",
"eess.SP",
"math.IT"
] | Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared channel reconfiguration capabilities. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-posi... | {
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2501.15881 | Multivariate Feature Selection and Autoencoder Embeddings of Ovarian
Cancer Clinical and Genetic Data | [
"cs.LG"
] | This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a nonlinear examination of an OC dataset using autoencoders, which compress data into a 3-dimensional latent space to detect potential intrinsic separability between... | {
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2501.15889 | Adaptive Width Neural Networks | [
"cs.LG",
"cs.AI"
] | For almost 70 years, researchers have mostly relied on hyper-parameter tuning to pick the width of neural networks' layers out of many possible choices. This paper challenges the status quo by introducing an easy-to-use technique to learn an unbounded width of a neural network's layer during training. The technique doe... | {
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2501.15890 | Complexity in Complexity: Understanding Visual Complexity Through
Structure, Color, and Surprise | [
"cs.CV",
"cs.AI"
] | Understanding human perception of visual complexity is crucial in visual cognition. Recently (Shen, et al. 2024) proposed an interpretable segmentation-based model that accurately predicted complexity across various datasets, supporting the idea that complexity can be explained simply. In this work, we investigate the ... | {
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2501.15891 | Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
Virtual Clothing Tasks | [
"cs.CV"
] | Image-based virtual try-on (VTON) aims to generate a virtual try-on result by transferring an input garment onto a target person's image. However, the scarcity of paired garment-model data makes it challenging for existing methods to achieve high generalization and quality in VTON. Also, it limits the ability to genera... | {
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2501.15893 | Benchmarking Quantum Reinforcement Learning | [
"quant-ph",
"cs.LG"
] | Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. T... | {
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2501.15897 | MPC4RL -- A Software Package for Reinforcement Learning based on Model
Predictive Control | [
"eess.SY",
"cs.SY"
] | In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC). Our aim is to make recent theoretical contributions from the literature more accessible to both the RL and MPC communities. We combine standard software tools developed by the RL community, such as G... | {
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2501.15899 | Asynchronous distributed collision avoidance with intention consensus
for inland autonomous ships | [
"eess.SY",
"cs.SY"
] | This paper focuses on the problem of collaborative collision avoidance for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision ... | {
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2501.15900 | Investigating the Sensitivity of Pre-trained Audio Embeddings to Common
Effects | [
"cs.LG"
] | In recent years, foundation models have significantly advanced data-driven systems across various domains. Yet, their underlying properties, especially when functioning as feature extractors, remain under-explored. In this paper, we investigate the sensitivity to audio effects of audio embeddings extracted from widely-... | {
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2501.15901 | Robust Mobile Robot Path Planning via LLM-Based Dynamic Waypoint
Generation | [
"cs.RO"
] | Mobile robot path planning in complex environments remains a significant challenge, especially in achieving efficient, safe and robust paths. The traditional path planning techniques like DRL models typically trained for a given configuration of the starting point and target positions, these models only perform well wh... | {
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2501.15907 | Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for
Speech Generation | [
"cs.SD",
"cs.CL",
"eess.AS"
] | Recent advancements in speech generation have been driven by the large-scale training datasets. However, current models fall short of capturing the spontaneity and variability inherent in real-world human speech, due to their reliance on audiobook datasets limited to formal read-aloud speech styles. To bridge this gap,... | {
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2501.15908 | Evidential Physics-Informed Neural Networks | [
"cs.LG",
"cs.AI",
"physics.comp-ph"
] | We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitt... | {
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2501.15910 | The Sample Complexity of Online Reinforcement Learning: A Multi-model
Perspective | [
"cs.LG",
"cs.SY",
"eess.SY",
"math.OC",
"stat.ML"
] | We study the sample complexity of online reinforcement learning for nonlinear dynamical systems with continuous state and action spaces. Our analysis accommodates a large class of dynamical systems ranging from a finite set of nonlinear candidate models to models with bounded and Lipschitz continuous dynamics, to syste... | {
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2501.15915 | Parametric Retrieval Augmented Generation | [
"cs.CL",
"cs.IR"
] | Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinations, outdated knowledge, and domain adaptation. In particular, existing RAG methods append relevant documents retrieved from external corpu... | {
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2501.15916 | Online Housing Market | [
"cs.GT",
"cs.AI"
] | This paper studies an online variant of the celebrated housing market problem, where each agent has a single house and seeks to exchange it for another based on her preferences. In this online setting, agents may arrive and depart at any time, meaning that not all agents are present on the housing market simultaneously... | {
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2501.15920 | Vienna Mosaic: Navigating Social Borders in a Melting Pot | [
"physics.soc-ph",
"cs.SI",
"physics.data-an"
] | Urban segregation poses a critical challenge in cities, exacerbating inequalities, social tensions, fears, and polarization. It emerges from a complex interplay of socioeconomic disparities and residential preferences, disproportionately impacting migrant communities. In this paper, using a comprehensive administrative... | {
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2501.15921 | CREATOR Case: PMSM and IM Electric Machine Data for Validation and
Benchmarking of Simulation and Modeling Approaches | [
"cs.CE"
] | This paper describes the complete sets of data of two different machines, a PMSM and an IM, that are made available to the public for modeling and simulation validation and benchmarking. For both machines, not only the complete sets of design parameters, i.e., motor geometry, electrical parameters, material properties,... | {
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2501.15922 | SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues
on GitHub | [
"cs.SE",
"cs.LG"
] | New contributors often struggle to find tasks that they can tackle when onboarding onto a new Open Source Software (OSS) project. One reason for this difficulty is that issue trackers lack explanations about the knowledge or skills needed to complete a given task successfully. These explanations can be complex and time... | {
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2501.15924 | Stabilization of an unstable reaction-diffusion PDE with input delay
despite state and input quantization | [
"eess.SY",
"cs.SY",
"math.AP"
] | We solve the global asymptotic stability problem of an unstable reaction-diffusion Partial Differential Equation (PDE) subject to input delay and state quantization developing a switched predictor-feedback law. To deal with the input delay, we reformulate the problem as an actuated transport PDE coupled with the origin... | {
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2501.15925 | Efficient Distillation of Deep Spiking Neural Networks for Full-Range
Timestep Deployment | [
"cs.LG",
"q-bio.NC"
] | Spiking Neural Networks (SNNs) are emerging as a brain-inspired alternative to traditional Artificial Neural Networks (ANNs), prized for their potential energy efficiency on neuromorphic hardware. Despite this, SNNs often suffer from accuracy degradation compared to ANNs and face deployment challenges due to fixed infe... | {
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2501.15928 | Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude
Economy Networking | [
"cs.NI",
"cs.AI",
"cs.LG"
] | Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehi... | {
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2501.15941 | SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster
Large-Scale Statistical Learning | [
"stat.ML",
"cs.LG"
] | Regularized empirical risk minimization (rERM) has become important in data-intensive fields such as genomics and advertising, with stochastic gradient methods typically used to solve the largest problems. However, ill-conditioned objectives and non-smooth regularizers undermine the performance of traditional stochasti... | {
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2501.15942 | TimeHF: Billion-Scale Time Series Models Guided by Human Feedback | [
"cs.LG"
] | Time series neural networks perform exceptionally well in real-world applications but encounter challenges such as limited scalability, poor generalization, and suboptimal zero-shot performance. Inspired by large language models, there is interest in developing large time series models (LTM) to address these issues. Ho... | {
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2501.15946 | Impact of Lead Time on Aggregate EV Flexibility for Congestion
Management Services | [
"eess.SY",
"cs.SY"
] | Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this... | {
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2501.15949 | Enhancing the Convergence of Federated Learning Aggregation Strategies
with Limited Data | [
"cs.LG"
] | The development of deep learning techniques is a leading field applied to cases in which medical data is used, particularly in cases of image diagnosis. This type of data has privacy and legal restrictions that in many cases prevent it from being processed from central servers. However, in this area collaboration betwe... | {
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2501.15953 | Understanding Long Videos via LLM-Powered Entity Relation Graphs | [
"cs.IR",
"cs.CV"
] | The analysis of extended video content poses unique challenges in artificial intelligence, particularly when dealing with the complexity of tracking and understanding visual elements across time. Current methodologies that process video frames sequentially struggle to maintain coherent tracking of objects, especially w... | {
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2501.15955 | Rethinking the Bias of Foundation Model under Long-tailed Distribution | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Long-tailed learning has garnered increasing attention due to its practical significance. Among the various approaches, the fine-tuning paradigm has gained considerable interest with the advent of foundation models. However, most existing methods primarily focus on leveraging knowledge from these models, overlooking th... | {
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2501.15957 | Inverse Reinforcement Learning via Convex Optimization | [
"cs.LG",
"cs.CE",
"math.OC",
"q-bio.NC"
] | We consider the inverse reinforcement learning (IRL) problem, where an unknown reward function of some Markov decision process is estimated based on observed expert demonstrations. In most existing approaches, IRL is formulated and solved as a nonconvex optimization problem, posing challenges in scenarios where robustn... | {
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2501.15963 | Evaluating Data Influence in Meta Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | As one of the most fundamental models, meta learning aims to effectively address few-shot learning challenges. However, it still faces significant issues related to the training data, such as training inefficiencies due to numerous low-contribution tasks in large datasets and substantial noise from incorrect labels. Th... | {
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2501.15968 | Multi-View Attention Syntactic Enhanced Graph Convolutional Network for
Aspect-based Sentiment Analysis | [
"cs.CL",
"cs.AI"
] | Aspect-based Sentiment Analysis (ABSA) is the task aimed at predicting the sentiment polarity of aspect words within sentences. Recently, incorporating graph neural networks (GNNs) to capture additional syntactic structure information in the dependency tree derived from syntactic dependency parsing has been proven to b... | {
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2501.15969 | An Explainable Disease Surveillance System for Early Prediction of
Multiple Chronic Diseases | [
"cs.LG",
"cs.AI"
] | This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI ... | {
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2501.15971 | REINFORCE-ING Chemical Language Models in Drug Design | [
"cs.LG"
] | Chemical language models, combined with reinforcement learning, have shown significant promise to efficiently traverse large chemical spaces in drug design. However, the performance of various RL algorithms and their best practices for practical drug design are still unclear. Here, starting from the principles of the R... | {
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2501.15972 | Flexible Blood Glucose Control: Offline Reinforcement Learning from
Human Feedback | [
"cs.AI",
"cs.LG"
] | Reinforcement learning (RL) has demonstrated success in automating insulin dosing in simulated type 1 diabetes (T1D) patients but is currently unable to incorporate patient expertise and preference. This work introduces PAINT (Preference Adaptation for INsulin control in T1D), an original RL framework for learning flex... | {
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2501.15973 | Integrating Probabilistic Trees and Causal Networks for Clinical and
Epidemiological Data | [
"cs.LG",
"q-bio.QM"
] | Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional Machine Learning (ML) models excel at predicting outcomes, such as identifying high risk patients, they are limited in addressing what-if questions about interventions. This... | {
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2501.15977 | Classification Error Bound for Low Bayes Error Conditions in Machine
Learning | [
"cs.LG",
"stat.ML"
] | In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replaced with a model distribution estimated from the training data in the Bayes decision rule. This substitu... | {
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2501.15981 | MatCLIP: Light- and Shape-Insensitive Assignment of PBR Material Models | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Assigning realistic materials to 3D models remains a significant challenge in computer graphics. We propose MatCLIP, a novel method that extracts shape- and lighting-insensitive descriptors of Physically Based Rendering (PBR) materials to assign plausible textures to 3D objects based on images, such as the output of La... | {
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2501.15987 | MultiPDENet: PDE-embedded Learning with Multi-time-stepping for
Accelerated Flow Simulation | [
"math.NA",
"cs.AI",
"cs.NA"
] | Solving partial differential equations (PDEs) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required. Machine learning can accelerate this process, but struggle with weak generalizability, interpretability, and data dependency, as well... | {
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2501.15990 | 3CEL: A corpus of legal Spanish contract clauses | [
"cs.CL"
] | Legal corpora for Natural Language Processing (NLP) are valuable and scarce resources in languages like Spanish due to two main reasons: data accessibility and legal expert knowledge availability. INESData 2024 is a European Union funded project lead by the Universidad Polit\'ecnica de Madrid (UPM) and developed by Ins... | {
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2501.15991 | Modeling and stability analysis of live systems with time-varying
dimension | [
"math.OC",
"cs.SY",
"eess.SY",
"math.DS"
] | A major limitation of the classical control theory is the assumption that the state space and its dimension do not change with time. This prevents analyzing and even formalizing the stability and control problems for open multi-agent systems whose agents may enter or leave the network, industrial processes where the se... | {
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} |
2501.15994 | Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using
YOLO11: From Model Training to Deployment in the Operating Room | [
"eess.IV",
"cs.CV"
] | Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for... | {
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2501.15995 | Brain-Inspired Decentralized Satellite Learning in Space Computing Power
Networks | [
"cs.LG",
"cs.DC",
"cs.NI",
"eess.SP"
] | Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bott... | {
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2501.15998 | Controllable Forgetting Mechanism for Few-Shot Class-Incremental
Learning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Class-incremental learning in the context of limited personal labeled samples (few-shot) is critical for numerous real-world applications, such as smart home devices. A key challenge in these scenarios is balancing the trade-off between adapting to new, personalized classes and maintaining the performance of the model ... | {
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2501.16002 | ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning | [
"cs.LG"
] | Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, most dynamic graph neural networks follow the traditional message-passing mechanism and extend it with time-based techniques. Despite their eff... | {
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2501.16003 | Improving Tropical Cyclone Forecasting With Video Diffusion Models | [
"cs.CV",
"physics.ao-ph"
] | Tropical cyclone (TC) forecasting is crucial for disaster preparedness and mitigation. While recent deep learning approaches have shown promise, existing methods often treat TC evolution as a series of independent frame-to-frame predictions, limiting their ability to capture long-term dynamics. We present a novel appli... | {
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2501.16004 | Epidemics on the Move: How Public Transport Demand and Capacity Shape
Disease Spread | [
"cs.SI",
"physics.soc-ph"
] | Understanding the dynamics of passenger interactions and their epidemiological impact throughout public transportation systems is crucial for both service efficiency and public health. High passenger density and close physical proximity has been shown to accelerate the spread of infectious diseases. During the COVID-19... | {
"Other": 0,
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} |
2501.16006 | Underactuated dexterous robotic grasping with reconfigurable passive
joints | [
"cs.RO"
] | We introduce a novel reconfigurable passive joint (RP-joint), which has been implemented and tested on an underactuated three-finger robotic gripper. RP-joint has no actuation, but instead it is lightweight and compact. It can be easily reconfigured by applying external forces and locked to perform complex dexterous ma... | {
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"cs.RO": 1,
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} |
2501.16008 | Gaussian credible intervals in Bayesian nonparametric estimation of the
unseen | [
"stat.ME",
"cs.LG",
"stat.ML",
"stat.OT"
] | The unseen-species problem assumes $n\geq1$ samples from a population of individuals belonging to different species, possibly infinite, and calls for estimating the number $K_{n,m}$ of hitherto unseen species that would be observed if $m\geq1$ new samples were collected from the same population. This is a long-standing... | {
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"cs.SD": 0,
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} |
2501.16011 | MEL: Legal Spanish Language Model | [
"cs.CL"
] | Legal texts, characterized by complex and specialized terminology, present a significant challenge for Language Models. Adding an underrepresented language, such as Spanish, to the mix makes it even more challenging. While pre-trained models like XLM-RoBERTa have shown capabilities in handling multilingual corpora, the... | {
"Other": 0,
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} |
2501.16018 | Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting | [
"cs.LG",
"cs.GT"
] | We consider the classical multi-armed bandit problem, but with strategic arms. In this context, each arm is characterized by a bounded support reward distribution and strategically aims to maximize its own utility by potentially retaining a portion of its reward, and disclosing only a fraction of it to the learning age... | {
"Other": 1,
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"cs.SD": 0,
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} |
2501.16022 | Freestyle Sketch-in-the-Loop Image Segmentation | [
"cs.CV"
] | In this paper, we expand the domain of sketch research into the field of image segmentation, aiming to establish freehand sketches as a query modality for subjective image segmentation. Our innovative approach introduces a "sketch-in-the-loop" image segmentation framework, enabling the segmentation of visual concepts p... | {
"Other": 0,
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} |
2501.16029 | FDLLM: A Text Fingerprint Detection Method for LLMs in Multi-Language,
Multi-Domain Black-Box Environments | [
"cs.CR",
"cs.AI"
] | Using large language models (LLMs) integration platforms without transparency about which LLM is being invoked can lead to potential security risks. Specifically, attackers may exploit this black-box scenario to deploy malicious models and embed viruses in the code provided to users. In this context, it is increasingly... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.16033 | PRISMe: A Novel LLM-Powered Tool for Interactive Privacy Policy
Assessment | [
"cs.HC",
"cs.AI"
] | Protecting online privacy requires users to engage with and comprehend website privacy policies, but many policies are difficult and tedious to read. We present PRISMe (Privacy Risk Information Scanner for Me), a novel Large Language Model (LLM)-driven privacy policy assessment tool, which helps users to understand the... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
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} |
2501.16037 | Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights
from the COOOL Challenge | [
"cs.CV"
] | This paper presents a novel approach for hazard analysis in dashcam footage, addressing the detection of driver reactions to hazards, the identification of hazardous objects, and the generation of descriptive captions. We first introduce a method for detecting driver reactions through speed and sound anomaly detection,... | {
"Other": 0,
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} |
2501.16046 | Revisiting Projection-Free Online Learning with Time-Varying Constraints | [
"cs.LG",
"stat.ML"
] | We investigate constrained online convex optimization, in which decisions must belong to a fixed and typically complicated domain, and are required to approximately satisfy additional time-varying constraints over the long term. In this setting, the commonly used projection operations are often computationally expensiv... | {
"Other": 0,
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"cs.SI": 0,
"cs.SY": 0
} |
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